SeqRFM: Fast RFM Analysis in Sequence Data
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910689627471872 |
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| author | Zheng, Yanxin Gan, Wensheng Chen, Zefeng Zhou, Pinlyu Fournier-Viger, Philippe |
| author_facet | Zheng, Yanxin Gan, Wensheng Chen, Zefeng Zhou, Pinlyu Fournier-Viger, Philippe |
| contents | In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, which combines sequential pattern mining with RFM models. SeqRFM considers each customer's recency (R), frequency (F), and monetary (M) scores to represent the significance of the customer and identifies sequences with high recency, high frequency, and high monetary value. A series of experiments demonstrate the superiority and effectiveness of the SeqRFM algorithm compared to the most advanced RFM algorithms based on sequential pattern mining. The source code and datasets are available at GitHub https://github.com/DSI-Lab1/SeqRFM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05317 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeqRFM: Fast RFM Analysis in Sequence Data Zheng, Yanxin Gan, Wensheng Chen, Zefeng Zhou, Pinlyu Fournier-Viger, Philippe Databases In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, which combines sequential pattern mining with RFM models. SeqRFM considers each customer's recency (R), frequency (F), and monetary (M) scores to represent the significance of the customer and identifies sequences with high recency, high frequency, and high monetary value. A series of experiments demonstrate the superiority and effectiveness of the SeqRFM algorithm compared to the most advanced RFM algorithms based on sequential pattern mining. The source code and datasets are available at GitHub https://github.com/DSI-Lab1/SeqRFM. |
| title | SeqRFM: Fast RFM Analysis in Sequence Data |
| topic | Databases |
| url | https://arxiv.org/abs/2411.05317 |